In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.
Int J Mol Sci
; 21(10)2020 May 19.
Article
in English
| MEDLINE | ID: covidwho-1934080
ABSTRACT
The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computer Simulation
/
Support Vector Machine
/
Intestines
Type of study:
Prognostic study
Limits:
Animals
/
Humans
Language:
English
Year:
2020
Document Type:
Article
Affiliation country:
Ijms21103582
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